The symptom domains of greatest interest in Alzheimer's disease (AD), cognition, function, and behavior, are also associated with executive function (EF). But EF cannot be directly observed; these symptom domains and EF have unknown measurement error in their assessment, and unknown interrelationships. Precise assessment of each area is important for efficient clinical studies of AD interventions and for public health purposes like screening for AD and dementia. Unlike regression analysis, latent variable analysis (LVA) methods can increase precision by explicitly modeling measurement error, and can model constructs (like EF) that are not directly observable. Additional precision will be derived from determining whether EF contributes significant explanatory power to models of cognitive aging and AD. This K01 therefore has two specific aims. Aim 1: Model measurement error in instruments that are common in clinical trials for AD using a Bayesian Network to differentiate measurement error from true change for non-demented elderly, persons [unreadable] with AD, and persons with incipient AD. Aim 2: Construct a statistical model of EF for persons with AD and cognitively normal elderly controls in order to: a. evaluate the latent factor structure in EF; and b. compare fits of latent variable models of EF. The best fitting model will address the hypothesis that EF contributes significant explanatory power for variance in cognitive, functional and behavioral symptoms and will suggest whether this is true for both normal elderly and persons with AD. These aims will contribute to precision and modeling of assessment in normal aging and AD; and to a better understanding of EF as an outcome in aging research and clinical studies of AD. The proposed K01 will develop deep and broad sets of skills in assessment and analysis, to be achieved with training in design and conduct of clinical trials, measurement theory, neuropsychology, and advanced statistical/LVA modeling. Mentoring from an experienced clinical trialist, Paul Aisen, MD, will be supplemented with expert consultation in these domains throughout the five year grant. The PI is a junior investigator in who wishes to extend her expertise in statistics to include LVA methods, and her background in cognitive science to include neuropsychological assessment and measurement. This K01 award will achieve these and so will move the PI closer to her long-term goal of becoming an independent researcher in cognitive aging and AD. [unreadable] [unreadable] [unreadable]